Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations3095
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory347.0 B

Variable types

Numeric12
Categorical4

Alerts

Jackpot is highly overall correlated with WinsHigh correlation
No. is highly overall correlated with Wins and 1 other fieldsHigh correlation
Wins is highly overall correlated with Jackpot and 2 other fieldsHigh correlation
YYYY is highly overall correlated with No. and 1 other fieldsHigh correlation
Day is highly imbalanced (56.3%) Imbalance
Wins is highly skewed (γ1 = 20.05977864) Skewed
No. is uniformly distributed Uniform
No. has unique values Unique
Wins has 1335 (43.1%) zeros Zeros

Reproduction

Analysis started2025-08-29 18:34:53.654867
Analysis finished2025-08-29 18:35:06.653376
Duration13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

No.
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct3095
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1548
Minimum1
Maximum3095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:06.830725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile155.7
Q1774.5
median1548
Q32321.5
95-th percentile2940.3
Maximum3095
Range3094
Interquartile range (IQR)1547

Descriptive statistics

Standard deviation893.59387
Coefficient of variation (CV)0.57725702
Kurtosis-1.2
Mean1548
Median Absolute Deviation (MAD)774
Skewness0
Sum4791060
Variance798510
MonotonicityStrictly decreasing
2025-08-29T19:35:06.952791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3095 1
 
< 0.1%
1063 1
 
< 0.1%
1037 1
 
< 0.1%
1036 1
 
< 0.1%
1035 1
 
< 0.1%
1034 1
 
< 0.1%
1033 1
 
< 0.1%
1032 1
 
< 0.1%
1031 1
 
< 0.1%
1030 1
 
< 0.1%
Other values (3085) 3085
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3095 1
< 0.1%
3094 1
< 0.1%
3093 1
< 0.1%
3092 1
< 0.1%
3091 1
< 0.1%
3090 1
< 0.1%
3089 1
< 0.1%
3088 1
< 0.1%
3087 1
< 0.1%
3086 1
< 0.1%

Day
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size184.5 KiB
Sat
1602 
Wed
1489 
Fri
 
2
Tue
 
1
Sun
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12380
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row Wed
2nd row Sat
3rd row Wed
4th row Sat
5th row Wed

Common Values

ValueCountFrequency (%)
Sat 1602
51.8%
Wed 1489
48.1%
Fri 2
 
0.1%
Tue 1
 
< 0.1%
Sun 1
 
< 0.1%

Length

2025-08-29T19:35:07.072125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-29T19:35:07.157716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sat 1602
51.8%
wed 1489
48.1%
fri 2
 
0.1%
tue 1
 
< 0.1%
sun 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3095
25.0%
S 1603
12.9%
a 1602
12.9%
t 1602
12.9%
e 1490
12.0%
W 1489
12.0%
d 1489
12.0%
F 2
 
< 0.1%
r 2
 
< 0.1%
i 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3095
25.0%
S 1603
12.9%
a 1602
12.9%
t 1602
12.9%
e 1490
12.0%
W 1489
12.0%
d 1489
12.0%
F 2
 
< 0.1%
r 2
 
< 0.1%
i 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3095
25.0%
S 1603
12.9%
a 1602
12.9%
t 1602
12.9%
e 1490
12.0%
W 1489
12.0%
d 1489
12.0%
F 2
 
< 0.1%
r 2
 
< 0.1%
i 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3095
25.0%
S 1603
12.9%
a 1602
12.9%
t 1602
12.9%
e 1490
12.0%
W 1489
12.0%
d 1489
12.0%
F 2
 
< 0.1%
r 2
 
< 0.1%
i 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

DD
Real number (ℝ)

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.723425
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:07.242613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.797243
Coefficient of variation (CV)0.55949916
Kurtosis-1.1926972
Mean15.723425
Median Absolute Deviation (MAD)8
Skewness0.0073736924
Sum48664
Variance77.391484
MonotonicityNot monotonic
2025-08-29T19:35:07.330127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 104
 
3.4%
9 103
 
3.3%
7 103
 
3.3%
2 103
 
3.3%
26 103
 
3.3%
19 103
 
3.3%
16 103
 
3.3%
28 102
 
3.3%
4 102
 
3.3%
11 102
 
3.3%
Other values (21) 2067
66.8%
ValueCountFrequency (%)
1 101
3.3%
2 103
3.3%
3 101
3.3%
4 102
3.3%
5 102
3.3%
6 100
3.2%
7 103
3.3%
8 101
3.3%
9 103
3.3%
10 101
3.3%
ValueCountFrequency (%)
31 59
1.9%
30 94
3.0%
29 94
3.0%
28 102
3.3%
27 100
3.2%
26 103
3.3%
25 99
3.2%
24 104
3.4%
23 102
3.3%
22 101
3.3%

MMM
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size181.5 KiB
Jul
266 
Mar
266 
May
265 
Aug
263 
Dec
262 
Other values (7)
1773 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9285
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAug
2nd rowAug
3rd rowAug
4th rowAug
5th rowAug

Common Values

ValueCountFrequency (%)
Jul 266
8.6%
Mar 266
8.6%
May 265
8.6%
Aug 263
8.5%
Dec 262
8.5%
Jan 260
8.4%
Apr 258
8.3%
Jun 257
8.3%
Oct 256
8.3%
Nov 251
8.1%
Other values (2) 491
15.9%

Length

2025-08-29T19:35:07.417390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jul 266
8.6%
mar 266
8.6%
may 265
8.6%
aug 263
8.5%
dec 262
8.5%
jan 260
8.4%
apr 258
8.3%
jun 257
8.3%
oct 256
8.3%
nov 251
8.1%
Other values (2) 491
15.9%

Most occurring characters

ValueCountFrequency (%)
a 791
 
8.5%
u 786
 
8.5%
J 783
 
8.4%
e 753
 
8.1%
M 531
 
5.7%
r 524
 
5.6%
A 521
 
5.6%
c 518
 
5.6%
n 517
 
5.6%
p 507
 
5.5%
Other values (12) 3054
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 791
 
8.5%
u 786
 
8.5%
J 783
 
8.4%
e 753
 
8.1%
M 531
 
5.7%
r 524
 
5.6%
A 521
 
5.6%
c 518
 
5.6%
n 517
 
5.6%
p 507
 
5.5%
Other values (12) 3054
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 791
 
8.5%
u 786
 
8.5%
J 783
 
8.4%
e 753
 
8.1%
M 531
 
5.7%
r 524
 
5.6%
A 521
 
5.6%
c 518
 
5.6%
n 517
 
5.6%
p 507
 
5.5%
Other values (12) 3054
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 791
 
8.5%
u 786
 
8.5%
J 783
 
8.4%
e 753
 
8.1%
M 531
 
5.7%
r 524
 
5.6%
A 521
 
5.6%
c 518
 
5.6%
n 517
 
5.6%
p 507
 
5.5%
Other values (12) 3054
32.9%

YYYY
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.2914
Minimum1994
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:07.490160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile1997
Q12003
median2010
Q32018
95-th percentile2024
Maximum2025
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6037364
Coefficient of variation (CV)0.0042798453
Kurtosis-1.1774712
Mean2010.2914
Median Absolute Deviation (MAD)7
Skewness-0.012877793
Sum6221852
Variance74.02428
MonotonicityDecreasing
2025-08-29T19:35:07.570880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2011 105
 
3.4%
2022 105
 
3.4%
2000 105
 
3.4%
2020 105
 
3.4%
2003 105
 
3.4%
2016 105
 
3.4%
2014 105
 
3.4%
2005 105
 
3.4%
2008 105
 
3.4%
2024 104
 
3.4%
Other values (22) 2046
66.1%
ValueCountFrequency (%)
1994 7
 
0.2%
1995 52
1.7%
1996 52
1.7%
1997 100
3.2%
1998 104
3.4%
1999 104
3.4%
2000 105
3.4%
2001 104
3.4%
2002 104
3.4%
2003 105
3.4%
ValueCountFrequency (%)
2025 67
2.2%
2024 104
3.4%
2023 104
3.4%
2022 105
3.4%
2021 104
3.4%
2020 105
3.4%
2019 104
3.4%
2018 104
3.4%
2017 104
3.4%
2016 105
3.4%

N1
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.728918
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:07.665471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median27
Q340
95-th percentile51
Maximum59
Range58
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.334343
Coefficient of variation (CV)0.57369861
Kurtosis-1.062006
Mean26.728918
Median Absolute Deviation (MAD)13
Skewness0.085275975
Sum82726
Variance235.14207
MonotonicityNot monotonic
2025-08-29T19:35:07.774444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 69
 
2.2%
35 69
 
2.2%
28 68
 
2.2%
48 67
 
2.2%
10 67
 
2.2%
44 67
 
2.2%
4 67
 
2.2%
33 67
 
2.2%
12 66
 
2.1%
25 65
 
2.1%
Other values (49) 2423
78.3%
ValueCountFrequency (%)
1 61
2.0%
2 52
1.7%
3 62
2.0%
4 67
2.2%
5 51
1.6%
6 53
1.7%
7 56
1.8%
8 62
2.0%
9 61
2.0%
10 67
2.2%
ValueCountFrequency (%)
59 19
0.6%
58 19
0.6%
57 19
0.6%
56 17
0.5%
55 17
0.5%
54 16
0.5%
53 17
0.5%
52 18
0.6%
51 17
0.5%
50 13
0.4%

N2
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.087561
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:07.891451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile51
Maximum59
Range58
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.52373
Coefficient of variation (CV)0.57309442
Kurtosis-1.0894958
Mean27.087561
Median Absolute Deviation (MAD)13
Skewness0.051176175
Sum83836
Variance240.98619
MonotonicityNot monotonic
2025-08-29T19:35:07.989979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 73
 
2.4%
43 73
 
2.4%
37 70
 
2.3%
44 69
 
2.2%
15 67
 
2.2%
25 67
 
2.2%
49 66
 
2.1%
12 66
 
2.1%
11 66
 
2.1%
38 66
 
2.1%
Other values (49) 2412
77.9%
ValueCountFrequency (%)
1 61
2.0%
2 64
2.1%
3 54
1.7%
4 62
2.0%
5 53
1.7%
6 60
1.9%
7 62
2.0%
8 61
2.0%
9 61
2.0%
10 44
1.4%
ValueCountFrequency (%)
59 18
0.6%
58 22
0.7%
57 20
0.6%
56 18
0.6%
55 19
0.6%
54 20
0.6%
53 14
0.5%
52 22
0.7%
51 17
0.5%
50 21
0.7%

N3
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.848142
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:08.088387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median27
Q339
95-th percentile52
Maximum59
Range58
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.437999
Coefficient of variation (CV)0.57501182
Kurtosis-1.048022
Mean26.848142
Median Absolute Deviation (MAD)13
Skewness0.090800226
Sum83095
Variance238.33181
MonotonicityNot monotonic
2025-08-29T19:35:08.198390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 76
 
2.5%
39 73
 
2.4%
17 72
 
2.3%
10 69
 
2.2%
40 68
 
2.2%
27 68
 
2.2%
11 67
 
2.2%
36 66
 
2.1%
18 66
 
2.1%
38 66
 
2.1%
Other values (49) 2404
77.7%
ValueCountFrequency (%)
1 50
1.6%
2 60
1.9%
3 76
2.5%
4 52
1.7%
5 56
1.8%
6 57
1.8%
7 57
1.8%
8 58
1.9%
9 61
2.0%
10 69
2.2%
ValueCountFrequency (%)
59 22
0.7%
58 25
0.8%
57 19
0.6%
56 22
0.7%
55 20
0.6%
54 21
0.7%
53 16
0.5%
52 20
0.6%
51 12
0.4%
50 10
 
0.3%

N4
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.292084
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:08.463826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median28
Q340
95-th percentile50
Maximum59
Range58
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.186792
Coefficient of variation (CV)0.55645409
Kurtosis-1.0830799
Mean27.292084
Median Absolute Deviation (MAD)13
Skewness0.023706881
Sum84469
Variance230.63864
MonotonicityNot monotonic
2025-08-29T19:35:08.563844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 72
 
2.3%
48 72
 
2.3%
19 70
 
2.3%
29 70
 
2.3%
11 70
 
2.3%
45 69
 
2.2%
20 69
 
2.2%
5 68
 
2.2%
35 67
 
2.2%
47 66
 
2.1%
Other values (49) 2402
77.6%
ValueCountFrequency (%)
1 52
1.7%
2 51
1.6%
3 49
1.6%
4 51
1.6%
5 68
2.2%
6 59
1.9%
7 50
1.6%
8 59
1.9%
9 55
1.8%
10 54
1.7%
ValueCountFrequency (%)
59 14
0.5%
58 20
0.6%
57 12
0.4%
56 13
0.4%
55 16
0.5%
54 20
0.6%
53 16
0.5%
52 20
0.6%
51 17
0.5%
50 20
0.6%

N5
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.440065
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:08.665432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median27
Q339
95-th percentile49.3
Maximum59
Range58
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.039343
Coefficient of variation (CV)0.56880887
Kurtosis-1.0374635
Mean26.440065
Median Absolute Deviation (MAD)12
Skewness0.079602516
Sum81832
Variance226.18185
MonotonicityNot monotonic
2025-08-29T19:35:08.774175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 79
 
2.6%
31 76
 
2.5%
23 73
 
2.4%
34 73
 
2.4%
36 71
 
2.3%
28 71
 
2.3%
30 70
 
2.3%
33 69
 
2.2%
27 69
 
2.2%
47 69
 
2.2%
Other values (49) 2375
76.7%
ValueCountFrequency (%)
1 54
1.7%
2 58
1.9%
3 55
1.8%
4 57
1.8%
5 62
2.0%
6 64
2.1%
7 62
2.0%
8 55
1.8%
9 67
2.2%
10 69
2.2%
ValueCountFrequency (%)
59 16
0.5%
58 12
0.4%
57 14
0.5%
56 18
0.6%
55 11
0.4%
54 14
0.5%
53 17
0.5%
52 18
0.6%
51 20
0.6%
50 15
0.5%

N6
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.049758
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:08.879026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile51
Maximum59
Range58
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.389957
Coefficient of variation (CV)0.56894992
Kurtosis-1.0864332
Mean27.049758
Median Absolute Deviation (MAD)13
Skewness0.047561443
Sum83719
Variance236.85079
MonotonicityNot monotonic
2025-08-29T19:35:08.980338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 77
 
2.5%
45 74
 
2.4%
40 73
 
2.4%
25 70
 
2.3%
38 70
 
2.3%
42 69
 
2.2%
7 68
 
2.2%
15 67
 
2.2%
30 67
 
2.2%
44 66
 
2.1%
Other values (49) 2394
77.4%
ValueCountFrequency (%)
1 55
1.8%
2 53
1.7%
3 60
1.9%
4 64
2.1%
5 54
1.7%
6 58
1.9%
7 68
2.2%
8 59
1.9%
9 62
2.0%
10 60
1.9%
ValueCountFrequency (%)
59 17
0.5%
58 21
0.7%
57 20
0.6%
56 13
0.4%
55 14
0.5%
54 22
0.7%
53 18
0.6%
52 23
0.7%
51 18
0.6%
50 21
0.7%

BN
Real number (ℝ)

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.559935
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:09.074639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median26
Q339
95-th percentile50
Maximum59
Range58
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.296516
Coefficient of variation (CV)0.57592444
Kurtosis-1.0601319
Mean26.559935
Median Absolute Deviation (MAD)13
Skewness0.095617433
Sum82203
Variance233.9834
MonotonicityNot monotonic
2025-08-29T19:35:09.173642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 81
 
2.6%
38 80
 
2.6%
6 74
 
2.4%
37 72
 
2.3%
8 72
 
2.3%
20 71
 
2.3%
28 71
 
2.3%
31 71
 
2.3%
21 70
 
2.3%
14 70
 
2.3%
Other values (49) 2363
76.3%
ValueCountFrequency (%)
1 58
1.9%
2 52
1.7%
3 56
1.8%
4 67
2.2%
5 63
2.0%
6 74
2.4%
7 47
1.5%
8 72
2.3%
9 62
2.0%
10 47
1.5%
ValueCountFrequency (%)
59 17
0.5%
58 18
0.6%
57 16
0.5%
56 17
0.5%
55 17
0.5%
54 22
0.7%
53 18
0.6%
52 15
0.5%
51 14
0.5%
50 23
0.7%

Jackpot
Real number (ℝ)

High correlation 

Distinct2836
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5123329.1
Minimum122510
Maximum52964701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:09.286102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122510
5-th percentile804529.6
Q12006853
median3800000
Q36865875
95-th percentile14447339
Maximum52964701
Range52842191
Interquartile range (IQR)4859022

Descriptive statistics

Standard deviation4780888.9
Coefficient of variation (CV)0.93316061
Kurtosis12.087396
Mean5123329.1
Median Absolute Deviation (MAD)1824471
Skewness2.673362
Sum1.5856703 × 1010
Variance2.2856898 × 1013
MonotonicityNot monotonic
2025-08-29T19:35:09.385750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 119
 
3.8%
3800000 61
 
2.0%
15000000 23
 
0.7%
20000000 17
 
0.5%
5000000 13
 
0.4%
2500000 8
 
0.3%
10000000 6
 
0.2%
7500000 6
 
0.2%
1666667 5
 
0.2%
3333334 4
 
0.1%
Other values (2826) 2833
91.5%
ValueCountFrequency (%)
122510 1
< 0.1%
145859 1
< 0.1%
152431 1
< 0.1%
202501 1
< 0.1%
216650 1
< 0.1%
225732 1
< 0.1%
229264 1
< 0.1%
235588 1
< 0.1%
252078 1
< 0.1%
264490 1
< 0.1%
ValueCountFrequency (%)
52964701 1
< 0.1%
47405023 1
< 0.1%
42659372 1
< 0.1%
39529852 1
< 0.1%
35410343 1
< 0.1%
35133888 1
< 0.1%
33282708 1
< 0.1%
33035323 1
< 0.1%
32690115 1
< 0.1%
32534188 1
< 0.1%

Wins
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5835218
Minimum0
Maximum133
Zeros1335
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2025-08-29T19:35:09.474195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum133
Range133
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4905061
Coefficient of variation (CV)2.2042678
Kurtosis680.74361
Mean1.5835218
Median Absolute Deviation (MAD)1
Skewness20.059779
Sum4901
Variance12.183633
MonotonicityNot monotonic
2025-08-29T19:35:09.559067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1335
43.1%
1 688
22.2%
2 428
 
13.8%
3 231
 
7.5%
4 160
 
5.2%
5 99
 
3.2%
6 41
 
1.3%
8 35
 
1.1%
7 30
 
1.0%
9 10
 
0.3%
Other values (14) 38
 
1.2%
ValueCountFrequency (%)
0 1335
43.1%
1 688
22.2%
2 428
 
13.8%
3 231
 
7.5%
4 160
 
5.2%
5 99
 
3.2%
6 41
 
1.3%
7 30
 
1.0%
8 35
 
1.1%
9 10
 
0.3%
ValueCountFrequency (%)
133 1
 
< 0.1%
57 1
 
< 0.1%
46 1
 
< 0.1%
32 1
 
< 0.1%
20 2
0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
0.1%
15 3
0.1%
14 2
0.1%

Machine
Categorical

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size208.7 KiB
Arthur
637 
Guinevere
617 
Lancelot
581 
Merlin
514 
Amethyst
172 
Other values (9)
574 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters37140
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Lancelot
2nd row Lotto 2
3rd row Lotto 2
4th row Lotto 2
5th row Lotto 2

Common Values

ValueCountFrequency (%)
Arthur 637
20.6%
Guinevere 617
19.9%
Lancelot 581
18.8%
Merlin 514
16.6%
Amethyst 172
 
5.6%
Topaz 157
 
5.1%
Sapphire 135
 
4.4%
Moonstone 66
 
2.1%
Opal 56
 
1.8%
Galahad 52
 
1.7%
Other values (4) 108
 
3.5%

Length

2025-08-29T19:35:09.638697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arthur 637
20.5%
guinevere 617
19.9%
lancelot 581
18.7%
merlin 514
16.5%
amethyst 172
 
5.5%
topaz 157
 
5.1%
sapphire 135
 
4.3%
moonstone 66
 
2.1%
opal 56
 
1.8%
galahad 52
 
1.7%
Other values (5) 120
 
3.9%

Most occurring characters

ValueCountFrequency (%)
14994
40.4%
e 3367
 
9.1%
r 2588
 
7.0%
n 1908
 
5.1%
t 1668
 
4.5%
i 1266
 
3.4%
u 1254
 
3.4%
l 1235
 
3.3%
a 1181
 
3.2%
h 996
 
2.7%
Other values (19) 6683
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14994
40.4%
e 3367
 
9.1%
r 2588
 
7.0%
n 1908
 
5.1%
t 1668
 
4.5%
i 1266
 
3.4%
u 1254
 
3.4%
l 1235
 
3.3%
a 1181
 
3.2%
h 996
 
2.7%
Other values (19) 6683
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14994
40.4%
e 3367
 
9.1%
r 2588
 
7.0%
n 1908
 
5.1%
t 1668
 
4.5%
i 1266
 
3.4%
u 1254
 
3.4%
l 1235
 
3.3%
a 1181
 
3.2%
h 996
 
2.7%
Other values (19) 6683
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14994
40.4%
e 3367
 
9.1%
r 2588
 
7.0%
n 1908
 
5.1%
t 1668
 
4.5%
i 1266
 
3.4%
u 1254
 
3.4%
l 1235
 
3.3%
a 1181
 
3.2%
h 996
 
2.7%
Other values (19) 6683
18.0%

Set
Categorical

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size184.5 KiB
4
372 
3
368 
6
361 
7
358 
2
358 
Other values (15)
1278 

Length

Max length4
Median length4
Mean length3.9954766
Min length3

Characters and Unicode

Total characters12366
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row 3
2nd row 2
3rd row 2
4th row 3
5th row 3

Common Values

ValueCountFrequency (%)
4 372
12.0%
3 368
11.9%
6 361
11.7%
7 358
11.6%
2 358
11.6%
1 350
11.3%
5 348
11.2%
8 311
10.0%
11 79
 
2.6%
10 68
 
2.2%
Other values (10) 122
 
3.9%

Length

2025-08-29T19:35:09.715255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 372
12.0%
3 368
11.9%
6 364
11.8%
7 362
11.7%
2 358
11.6%
1 351
11.3%
5 350
11.3%
8 315
10.2%
11 79
 
2.6%
10 68
 
2.2%
Other values (5) 108
 
3.5%

Most occurring characters

ValueCountFrequency (%)
9086
73.5%
1 615
 
5.0%
4 392
 
3.2%
2 376
 
3.0%
3 368
 
3.0%
6 364
 
2.9%
7 362
 
2.9%
5 350
 
2.8%
8 315
 
2.5%
0 68
 
0.5%
Other values (3) 70
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9086
73.5%
1 615
 
5.0%
4 392
 
3.2%
2 376
 
3.0%
3 368
 
3.0%
6 364
 
2.9%
7 362
 
2.9%
5 350
 
2.8%
8 315
 
2.5%
0 68
 
0.5%
Other values (3) 70
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9086
73.5%
1 615
 
5.0%
4 392
 
3.2%
2 376
 
3.0%
3 368
 
3.0%
6 364
 
2.9%
7 362
 
2.9%
5 350
 
2.8%
8 315
 
2.5%
0 68
 
0.5%
Other values (3) 70
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9086
73.5%
1 615
 
5.0%
4 392
 
3.2%
2 376
 
3.0%
3 368
 
3.0%
6 364
 
2.9%
7 362
 
2.9%
5 350
 
2.8%
8 315
 
2.5%
0 68
 
0.5%
Other values (3) 70
 
0.6%

Interactions

2025-08-29T19:35:05.278566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.414607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.440611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.565755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.508141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.393373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.328772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.395659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.326958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.384639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.443577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.399453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.357691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.519176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.518087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.643420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.591475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.469012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.406568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.470351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.421673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.478730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.521316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.475207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.442207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.608671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.609897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.713773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.663951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.552301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.628973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.542536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.520911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.555210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.590034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.544144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.517242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.710877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.701858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.799109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.739125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.645262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.704764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.619749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.614647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.633335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.665912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.618724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.588932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.788793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.771634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.870458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.811967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.715699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.775702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.693539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.700845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.704086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.738896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.693814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.658231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:54.866781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.847134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.944441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.885035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.784711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.847551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.765551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.778701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.930537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.812328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.776410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-29T19:34:57.027880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-29T19:34:59.924648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-29T19:35:03.078998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.976267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.921775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:06.034647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-29T19:35:06.278540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:55.364837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:56.489605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:57.414724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:58.321771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:34:59.249144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:00.325406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:01.242782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:02.294424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:03.369502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:04.280531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T19:35:05.206478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-29T19:35:09.787092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DayBNDDJackpotMMMMachineN1N2N3N4N5N6No.SetWinsYYYY
Day1.0000.0000.0070.0740.0010.1020.0000.0000.0000.0160.0040.0080.0480.0000.0000.065
BN0.0001.0000.0170.0450.0250.050-0.0160.0020.015-0.028-0.017-0.0070.1110.033-0.0680.111
DD0.0070.0171.000-0.0240.0000.0000.0150.0090.0420.005-0.0030.021-0.0000.0000.016-0.003
Jackpot0.0740.045-0.0241.0000.0310.0440.1300.1400.1080.1380.0730.1610.3200.000-0.5910.319
MMM0.0010.0250.0000.0311.0000.0630.0000.0160.0270.0340.0000.0100.0000.0200.0000.000
Machine0.1020.0500.0000.0440.0631.0000.0300.0390.0430.0390.0210.0500.3680.0730.0330.433
N10.000-0.0160.0150.1300.0000.0301.000-0.0220.0090.020-0.0160.0000.0990.040-0.1260.101
N20.0000.0020.0090.1400.0160.039-0.0221.0000.0100.012-0.0320.0040.1260.027-0.1750.126
N30.0000.0150.0420.1080.0270.0430.0090.0101.0000.0070.014-0.0140.1090.035-0.1380.109
N40.016-0.0280.0050.1380.0340.0390.0200.0120.0071.0000.0140.0090.1290.037-0.1570.128
N50.004-0.017-0.0030.0730.0000.021-0.016-0.0320.0140.0141.000-0.0020.0740.031-0.1090.074
N60.008-0.0070.0210.1610.0100.0500.0000.004-0.0140.009-0.0021.0000.1070.000-0.1600.107
No.0.0480.111-0.0000.3200.0000.3680.0990.1260.1090.1290.0740.1071.0000.183-0.5840.999
Set0.0000.0330.0000.0000.0200.0730.0400.0270.0350.0370.0310.0000.1831.0000.0000.196
Wins0.000-0.0680.016-0.5910.0000.033-0.126-0.175-0.138-0.157-0.109-0.160-0.5840.0001.000-0.583
YYYY0.0650.111-0.0030.3190.0000.4330.1010.1260.1090.1280.0740.1070.9990.196-0.5831.000

Missing values

2025-08-29T19:35:06.426189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-29T19:35:06.547487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

No.DayDDMMMYYYYN1N2N3N4N5N6BNJackpotWinsMachineSet
03095Wed20Aug202519103244554952089130Lancelot3
13094Sat16Aug2025111151945483738996780Lotto 22
23093Wed13Aug2025116274150555420000000Lotto 22
33092Sat9Aug2025227293844512373945211Lotto 23
43091Wed6Aug202559173147583553020850Lotto 23
53090Sat2Aug202533151030505640078370Lotto 24
63089Wed30Jul20253732151873820000000Lotto 24
73088Sat26Jul20252842245723918114380160Lotto 21
83087Wed23Jul202548311211692287351660Lotto 21
93086Sat19Jul202525465729401572987680Lotto 22
No.DayDDMMMYYYYN1N2N3N4N5N6BNJackpotWinsMachineSet
308510Sat21Jan199547616313020413735717Guinevere4
30869Sat14Jan19952338177324248122510133Guinevere1
30878Sat7Jan1995213225252246100000000ArthurB
30887Sat31Dec1994174436329421665540851ArthurB
30896Sat24Dec19942729393442677895571ArthurB
30905Sat17Dec19941333851493034033102ArthurA
30914Sat10Dec199426474943353828178800031GuinevereB
30923Sat3Dec19942111173029403169065720ArthurB
30932Sat26Nov1994166443112153717609664ArthurB
30941Sat19Nov19943035441422108392547GuinevereA